2027 ya da 2028 yılında satışa sunulması beklenen PlayStation 6 için güvenilir kaynak Kepler_L2 tarafından yeni iddialar ortaya atıldı. Modelin disk sürücüsü olmadan 1 TB SSD ile geleceğine ve 700 dolardan satışa sunulabileceğine (mevcut parça maliyeti 760 dolar olarak hesaplanıyor; toplu alım indirimiyle Sony’nin konsolu 700 dolara satabileceği düşünülüyor) işaret eden kaynak, PS6 SDK’sinin nöral doku sıkıştırma altyapısına destek vermesi halinde oyun boyutlarının hatırı sayılır oranda azaltılabileceğini de belirtiyor. “Oyun boyutları PS5’tekinden bile daha küçük olabilir.” diyen kaynak, 1 TB SSD’nin bu konuda beklenen adım atılırsa büyük bir sorun olmayacağını aktarıyor. SSD ve RAM fiyatlarındaki artış nedeniyle PS6’ya satış fiyatını aşırı artırmadan 2 TB SSD koyması çok olası görünmeyen Sony, geçtiğimiz hafta PlayStation 5 serisine zam yaptığını duyurmuştu.

PlayStation 6 için yeni iddialar ortaya atıldı yazısı ilk olarak Teknoloji haberleri – LOG üzerinde yayınlandı.

Why AWS RDS Is Expensive Once Your Product Starts Growing

If you’ve ever opened your cloud bill and paused a little longer than usual, you’re not alone. At some point in every growing product’s journey, teams start asking the same quiet question: why is RDS so expensive, especially when it didn’t feel that way at the start.

The direct answer is that AWS RDS is expensive because you’re not paying for just a database. You’re paying for a stack of components such as compute, storage, replication, backups, and high availability which are all billed independently.

As your architecture matures, those layers compound rather than grow linearly. That’s why AWS RDS becomes expensive faster than most teams expect.

For teams hitting these limits, newer deployment models like BYOC (Bring Your Own Cloud) are also starting to emerge as an alternative.

Flexera’s 2026 State of the Cloud Report surveyed 753 cloud decision-makers worldwide. The results showed that 29% of cloud spend is currently wasted, and managing cloud costs remains the number one challenge for 85% of organisations. RDS over-provisioning is one of the most consistent contributors to that figure.

Despite this, RDS is popular for a good reason. It’s simple to launch, easy to manage, and removes real operational burden from small teams. In the early stages, that convenience is a genuinely smart trade-off.

This is exactly why many teams eventually start exploring how growing startups evaluate managed vs self-hosted database trade-offs.

This article is not about criticising AWS. It’s about explaining, with verified 2026 pricing data, exactly why AWS RDS is expensive as your product scales and what your options actually are when you reach that point.

Why AWS RDS Is Expensive: How the Pricing Model Actually Works

Understanding why AWS RDS is expensive starts with one key insight: you are never paying for a single thing. You are paying for a stack of services, each billed on its own meter.

Here is what that stack looks like in practice:

1. – 1. Instance hours – compute (CPU and RAM) billed per second
2. – 2. Storage – gp2, gp3, or io1, charged per GB per month
3. – 3. Automated backup storage – free up to 100% of database size, billed beyond that
4. – 4. Provisioned IOPS – charged separately from storage on io1
5. – 5. Multi-AZ standby – a full second instance billed at the same rate
6. – 6. Read replicas – each one is an independent full-price instance
7. – 7. Manual snapshots – persist indefinitely, accumulate silently
8. – 8. Data transfer – cross-AZ replication, outbound traffic

Key principle:

Each component is billed independently. Individually they feel small. Together, they explain why AWS RDS is expensive at scale.

AWS documents this on its official pricing page. The components are listed clearly.

What the pricing page doesn’t show is how they stack on top of each other and why RDS is expensive specifically when multiple layers are active at the same time.

The Growth Inflection Point That Makes AWS RDS Expensive

Most teams don’t ask why RDS is expensive in the early stage. The costs feel reasonable, and the convenience is real.

The question surfaces when:

1. The product stabilises and retains paying customers
2. Downtime becomes financially and reputationally costly
3. Customer expectations around uptime and performance increase
4. Compliance or data residency requirements enter the picture
   That’s the real inflection point where teams first understand why RDS is expensive and it’s not driven by traffic alone, it’s driven by architecture maturity.

What actually changes at the growth stage

The shift isn’t just more users. It’s the architecture decisions those users demand:

1. Multi-AZ enabled for production reliability
2. One or more read replicas for query performance
3. A staging environment that mirrors production
4. Longer backup retention windows (7 to 35 days)
5. Higher IOPS as write-heavy workloads grow
   Each of these is a separate billing layer and this compounding is precisely why RDS is expensive at the growth stage. Combined, they can turn a $150/month database into a $700–$900/month database without a single change to your application code.

The insight: AWS RDS is not expensive at the beginning. It becomes expensive when reliability becomes non-negotiable and your architecture starts to reflect that.

This is the moment teams start asking whether managed databases are still the right model for their stage.

What AWS RDS Actually Costs at Scale: Verified 2026 Pricing

To show exactly why AWS RDS is expensive in practice, the following tables use verified AWS on-demand pricing for us-east-1 (N. Virginia) as of March 2026, for a PostgreSQL workload on db.m6g.large. All figures are cross-referenced against Vantage EC2 Instances and AWS official pricing documentation.

Early stage: Single AZ, light workload

Pricing based on AWS on-demand rates, us-east-1 (N. Virginia), March 2026. You can Verify current rates on official Amazon RDS pricing page.

Growth stage: Multi-AZ, read replica, staging environment

Pricing based on AWS on-demand rates, us-east-1 (N. Virginia), March 2026. Figures will vary by region, instance generation, and storage type. Verify current rates on the official Amazon RDS pricing page.

These figures are illustrative for a db.m6g.large workload in us-east-1.

To model your specific instance type, storage type, region, and backup retention, use the AWS Pricing Calculator.

Note: The io1 / Provisioned IOPS row only applies if your workload requires guaranteed IOPS beyond what gp3 provides. Most teams running standard workloads on gp3 will not see this charge. If you are on Reserved Instances, compute costs reduce by 30–40% on a 1-year term and up to 60% on a 3-year term but storage, IOPS, backup, and data transfer charges remain unchanged regardless of reservation.

The key point: nothing in this table is accidental or wasteful. Every line item exists because your product genuinely needs it. These are the costs of running a production-grade database and because they are structural, no amount of optimisation will make them disappear. That is the real reason teams start exploring alternatives like BYOC, where you keep your infrastructure but avoid managed service pricing layers.

Can You Reduce AWS RDS Cost? Yes, But Here’s Where It Stops Helping

One of the most common questions once teams understand why RDS is expensive is whether optimisation can fix it.

Yes, there are real optimisations available and teams should pursue them before drawing conclusions. Here is what actually works, and where the ceiling is.

1. Right-size your instances

Most teams set up their RDS instance at launch, pick a size that feels safe, and never look at it again. According to Cast AI’s 2025 Kubernetes Cost Benchmark Report, the average cloud CPU utilisation across 2,100+ organisations is just 10%. You are very likely paying for compute you are not using.

The fix is simple: open Amazon CloudWatch, check your CPU utilisation and available memory over the past two weeks, and see what your database is actually doing. If your instance is sitting at 20% CPU, dropping one size down handles the same workload at roughly half the compute cost.

Before making any changes, set up CloudWatch alarms on three metrics: DatabaseConnections, CPUUtilization, and FreeableMemory. Measure first. Then resize.

2. Switch from gp2 to gp3 storage

If you are still on gp2 storage, switching to gp3 is the easiest cost win available to you. Same price per GB, but gp3 gives you 3,000 IOPS and 125 MB/s throughput included at no extra charge. AWS lets you migrate in place, no downtime, no risk.

One thing to check first: if your database is larger than 400 GB, IOPS and throughput minimums increase. Run your numbers through the AWS Pricing Calculator before switching to make sure the move still saves you money.

3. Review backup retention and snapshot accumulation

Most teams set their backup retention window once during setup and never revisit it. If you are on a 35-day retention window, ask yourself honestly: has your team ever needed to restore from a 3-week-old backup? For most products, 14 or 21 days covers the real risk window, and the cost difference is meaningful.

Also check your manual snapshots. Automated snapshots expire automatically when you delete them or when the retention window passes. Manual snapshots do not.

They sit there quietly, accumulating cost every single month, and most teams have no idea how many they have. Go into your RDS console right now and audit them: you may be surprised.

4. Use Reserved Instances for stable workloads

Reserved Instances let you commit to a 1 or 3-year term in exchange for a discount roughly around 30–40% on a 1-year term, up to 60% on a 3-year term.

The catch is flexibility: if your instance type or region needs to change, you lose the discount entirely.

The bigger thing to understand is what Reserved Instances actually discount and what they don’t.

They only reduce your compute charge. Storage, IOPS, backups, and data transfer are billed exactly the same whether you are on-demand or reserved. If those are the lines driving your bill, Reserved Instances will not help.

Where optimisation reaches its ceiling

Here is the uncomfortable truth: if your system genuinely needs Multi-AZ, a read replica, a staging environment, and reliable backups, you are not doing anything wrong. You have simply hit the ceiling of what optimisation can do. The costs are not inefficiency. They are the price of the model itself.

At that point, tweaking settings stops being the answer. The real question becomes: is RDS the right model for where your product is today?

For teams at this stage, the real decision comes down to understanding the managed vs self-hosted database trade-offs for growing startups and how each model holds up as your system scales.

What Growing Teams Do When Optimisation Stops Working

ou have right-sized the instances. Switched to gp3. Audited the snapshots. And the bill barely moved.

That is not a you problem. That is a model problem.

There are three paths forward from here. Most teams only discover the third one after spending months on the first two.

Option 1: Stay on RDS

For a lot of teams, this is the right call. If your database bill is somewhere between 5–10% of revenue and your team has no one dedicated to infrastructure, the convenience RDS gives you is genuinely worth the price. Managed means no late-night alerts about replication lag. No DBA on call. No one losing a weekend to a failed upgrade.

The only question worth asking is: does the math still work at twice your current scale?

If the answer is yes, stay. If you are not sure – keep reading.

Option 2: Self-host on EC2 or EKS

This is the classic escape route. Drop RDS, run PostgreSQL directly on EC2, and pay raw compute and storage rates with no managed service markup. At scale, that can cut your database infrastructure costs by 40–60%.

The savings are real. So is the cost that doesn’t show up on the bill.

Upgrades, failover, monitoring, backups, replication, all of it becomes your team’s problem. For a team with a dedicated DevOps engineer or DBA, that trade-off can make sense. For everyone else, you are essentially trading a predictable monthly charge for an unpredictable engineering burden.

For years, this was the only alternative to RDS. Managed convenience on one side. Full control with full responsibility on the other. Pick one.

That’s no longer the whole picture. For teams evaluating this shift, the real question is how the managed vs self-hosted database trade-offs for growing startups actually play out once you factor in both cost and operational complexity.

Option 3: The model that changes the equation entirely

What if you didn’t have to choose between “pay AWS to manage everything” and “manage everything yourself”?

That’s exactly what the BYOC (Bring Your Own Cloud) model is. Your database runs inside your own cloud account, your AWS, your GCP, your Azure.

You pay cloud-native compute and storage rates directly, with no managed service markup sitting on top. And a platform handles everything else: provisioning, failover, backups, upgrades, and monitoring.

You get the cost structure of self-hosted. Without the operational burden that makes self-hosted painful.

In practice, that means:

1. No managed service premium on every GB and every instance hour
2. Full visibility into your own infrastructure: no black box
3. No vendor lock-in: your database lives in your account, not theirs
4. No DevOps hire needed: the platform handles operations for you

This is the model SelfHost.dev is built around. Instead of your data and costs flowing through a managed platform’s infrastructure, everything runs in your own cloud while SelfHost handles the operational layer that used to justify the RDS premium.

Teams that move to SelfHost typically reduce their database infrastructure costs by up to 60%, without removing a single reliability feature.

And there’s something else worth knowing.

SelfHost recently launched an MCP server, meaning your AI coding agent (Claude, Cursor, Windsurf, VS Code) can now talk directly to your managed database without leaving your IDE. Query it, inspect it, migrate it, monitor it, all from inside the tools your team is already using.

For teams already using Claude or Cursor, this means your AI agent can query, inspect, and monitor your database directly without switching tools or writing a single integration.

This is especially worth exploring if you are:

1. Spending $800–$1,000/month or more on RDS and watching that number climb
2. Running multiple environments and feeling the compounding cost of each one
3. Building with AI tools and want your database natively connected to your coding agent
4. Done with vendor lock-in and want infrastructure that belongs to your team, not your cloud provider

When It Makes Strategic Sense to Reconsider Why RDS Is Expensive for You

There is no magic number that tells you it is time to reconsider. But there is a pattern.

It starts with someone flagging the database bill in a finance review. Then an engineer spends half a sprint trying to optimise it. Then leadership starts asking why infrastructure costs are scaling faster than revenue. By that point, the question is no longer operational, it is strategic.

This is happening across the industry right now. According to Flexera’s 2026 State of the Cloud Report, 29% of cloud spend is currently wasted, up for the first time in five years. Managing cloud costs remains the number one challenge for 85% of organisations surveyed. Cloud budgets are already exceeding forecasts by 17% on average.

The teams feeling that pressure most acutely are not the ones who made bad decisions. They are the ones whose products grew and whose architecture matured to match. RDS did not get more expensive. The setup did.

Here are the signals that tell you it is time to have that conversation seriously:

1. Your RDS bill has become one of the top 3 line items in your cloud spend
2. You are running 3 or more environments and feeling the cost of each one
3. You cannot predict next month’s database bill with any confidence
4. Engineering cycles are going toward managing costs instead of building product
5. You have data residency or multi-cloud requirements that RDS makes complicated
   If you checked two or more of those, you are not alone, and you are not stuck.

One bootstrapped SaaS team was in exactly this position. They did not strip back their architecture. They did not sacrifice reliability. They simply changed the model underneath it and cut their AWS database spend by 60% – how they approached that shift is worth understanding if you’re facing similar cost pressure.

The teams who figure this out early do not do it by working harder on optimisation. They do it by asking a better question, not “how do we reduce this bill?” but “are we on the right model for where our product is today?”

That question, asked early enough, is worth more than any discount AWS will ever give you.

Why AWS RDS Is Expensive Is the Right Question to Be Asking

Why AWS RDS Is Expensive Is the Right Question to Be Asking

You Already Know Why. Now Decide What to Do About It.

Early-stage teams optimise for speed. Growth-stage teams start asking why AWS RDS is expensive. Mature teams optimise for leverage.

The difference between the second and third stage is not technical knowledge. It is timing. The teams that move decisively, that ask the right question before the bill becomes a crisis are the ones that end up with a database strategy that scales with their business instead of fighting it.

You now know exactly whyAWS RDS is expensive. You know which layers compound. You know where optimisation helps and where it hits a ceiling. You know there is a third model most teams never hear about early enough.

The only question left is whether you act on it now or wait until the bill forces your hand.

If you’re still trying to reduce RDS costs without taking on full operational burden, it’s worth understanding how BYOC compares to traditional managed databases.

What you will learn

This guide walks through every step of integrating CodeRabbit with GitHub – from installing the GitHub App to configuring advanced review behavior with .coderabbit.yaml. By the end, you will have CodeRabbit automatically reviewing every pull request in your selected repositories, posting inline comments on code issues, and learning from your team’s feedback to improve over time.

Here is what this guide covers:

• Installing the CodeRabbit GitHub App and granting the correct permissions
• Selecting which repositories CodeRabbit should review
• Understanding the permissions CodeRabbit requests and why each one is needed
• Triggering your first automated PR review
• Customizing review behavior with .coderabbit.yaml including review profiles, path filters, and natural language instructions
• Advanced configuration for auto-review settings, monorepos, and integrations
• Troubleshooting common issues with the GitHub integration
• Alternative tools if CodeRabbit does not fit your workflow

If you are looking for a broader overview of CodeRabbit’s features and pricing before setting up the GitHub integration, start with our CodeRabbit review or CodeRabbit pricing breakdown.

Prerequisites

Before setting up the CodeRabbit GitHub integration, confirm you have the following:

• A GitHub account (personal or organization) with at least one repository
• Admin or owner permissions on the repositories where you want to install CodeRabbit – this is required to authorize GitHub Apps
• At least one open pull request or the ability to create one for testing the integration (optional but recommended)
• A modern web browser – the entire setup happens through the browser with no local tooling required

No CI/CD pipeline, Docker installation, API keys, or command-line tools are needed. CodeRabbit runs on its own infrastructure as a native GitHub App, which means it does not consume your GitHub Actions minutes or require workflow YAML files.

Step 1 – Install the CodeRabbit GitHub App

The GitHub App installation is the core of the CodeRabbit GitHub integration. This is what connects CodeRabbit’s AI review engine to your repositories and pull requests. Unlike tools that run as GitHub Actions, CodeRabbit operates as a standalone GitHub App – similar to how Slack, Jira, or Vercel integrate with GitHub.

Here is how to install it:

1. Open coderabbit.ai in your browser and click the “Get Started Free” button
2. Select GitHub as your Git platform
3. Authorize CodeRabbit using the standard GitHub OAuth screen – this grants CodeRabbit access to your identity so it can create your account
4. After landing on the CodeRabbit dashboard, click “Add Repositories” or “Install GitHub App”
5. GitHub will redirect you to the App installation page

The OAuth authorization in step 3 and the App installation in step 4 are two separate operations. The OAuth grants CodeRabbit access to your identity (who you are). The App installation grants it access to your repositories and pull requests (what it can read and comment on). Both are required for the integration to work.

If you are installing CodeRabbit for an organization rather than a personal account, you may need an organization owner to approve the installation. If you see a “Request” button instead of “Install,” the request will be sent to your organization owner for approval. Ask them to check the GitHub organization settings under Third-party access to approve the CodeRabbit app.

For a deeper walkthrough of the sign-up and account creation process, see our CodeRabbit setup guide.

Step 2 – Select repositories

During installation, GitHub asks you to choose which repositories CodeRabbit should have access to. You have two options:

• All repositories – CodeRabbit gets access to every repository in the organization or account, including any repositories created in the future. This is convenient for teams that want full coverage without manual configuration, but it means CodeRabbit will start reviewing PRs on every repository immediately.
• Only select repositories – You choose specific repositories from a searchable list. This is the recommended approach for teams that want to start with a pilot before expanding. You can always add more repositories later from your GitHub settings or the CodeRabbit dashboard.

For most teams, the best approach is to start with two or three active repositories that have regular pull request activity. This lets you evaluate the review quality, tune your .coderabbit.yaml configuration, and get team feedback before rolling CodeRabbit out more broadly.

After selecting your repositories, click “Install” to complete the installation. The CodeRabbit App now appears in your GitHub organization settings under Settings, then GitHub Apps (or in your personal account under Settings, then Applications, then Installed GitHub Apps).

Managing repositories after installation

You can change which repositories CodeRabbit has access to at any time:

• From GitHub: Go to your organization or account settings, navigate to GitHub Apps, find CodeRabbit, and click “Configure.” You can add or remove repositories from this screen.
• From CodeRabbit: Log in to app.coderabbit.ai and navigate to the “Repositories” tab. Toggle individual repositories on or off to control which ones CodeRabbit actively reviews.

The GitHub-level setting controls access (can CodeRabbit see the repository), while the CodeRabbit dashboard setting controls activation (should CodeRabbit review PRs on this repository). A repository must have both access and activation enabled for reviews to work.

Step 3 – Configure permissions

The CodeRabbit GitHub App requests a specific set of permissions during installation. Understanding what each permission does helps you evaluate whether the integration meets your security and compliance requirements.

Here are the permissions CodeRabbit requests and why each one is necessary:

CodeRabbit does not request write access to your repository contents. It cannot push commits, create branches, modify files, or merge pull requests. The integration is strictly read-only for your code and write-only for review comments.

For organizations with strict security policies, CodeRabbit also offers SOC 2 Type II compliance on its Enterprise plan and provides data processing agreements. Code is processed in memory during review and is not stored permanently on CodeRabbit’s servers.

Step 4 – First PR review

Once the GitHub App is installed and repositories are selected, open a pull request to trigger your first CodeRabbit review. This is where you verify that the integration is working correctly.

1. Create a new branch in one of your enabled repositories
2. Make a meaningful code change – adding a new function, refactoring a method, or fixing a bug works well for testing
3. Push the branch and open a pull request targeting your default branch
4. Wait 1 to 5 minutes for CodeRabbit to complete its review

When the review finishes, CodeRabbit posts several elements on your pull request:

A walkthrough summary appears as the first comment on the PR. It describes what changed across all files, organized by file and purpose. This summary helps reviewers quickly understand the scope of the PR without reading every line of the diff.

Inline review comments appear directly on specific lines in the diff, just like comments from a human reviewer. Each comment describes the issue, explains why it matters, and often includes a suggested fix. On the Pro plan, these suggestions appear as GitHub suggested changes that you can commit with one click.

A review status indicator shows whether the review is complete and how many comments were generated. This appears in the PR’s checks section.

If CodeRabbit does not post any comments within 5 minutes, jump to the troubleshooting section below. Common causes include the PR being a draft, the repository not being activated in the CodeRabbit dashboard, or permission issues with the GitHub App installation.

Interacting with CodeRabbit comments

CodeRabbit comments are not one-way notifications – they are the start of a conversation. You can reply to any comment to ask for clarification, request an alternative approach, or explain why the suggestion does not apply to your codebase:

• Ask why: “Can you explain why this is a security risk? We validate this input in the middleware layer.”
• Request alternatives: “This approach would break our caching layer. What is another way to handle this?”
• Trigger actions: Comment @coderabbitai review on the PR to trigger a full re-review, or @coderabbitai resolve to dismiss a specific comment

Every interaction teaches CodeRabbit about your preferences. When you dismiss a comment with an explanation, CodeRabbit stores that as a learning and adjusts future reviews accordingly. Teams that actively reply during the first two weeks of adoption see significantly fewer false positives by the end of the first month.

Step 5 – Customize with .coderabbit.yaml

The .coderabbit.yaml file is where you tailor CodeRabbit’s GitHub integration to match your team’s workflow. While CodeRabbit works out of the box with sensible defaults, the configuration file gives you fine-grained control over what gets reviewed, how strictly, and with what focus areas.

Create a file named .coderabbit.yaml in the root of your repository and commit it to your default branch (usually main):

# .coderabbit.yaml
language: en-US

reviews:
  profile: chill
  request_changes_workflow: false
  high_level_summary: true
  poem: false
  review_status: true
  collapse_walkthrough: false
  sequence_diagrams: true
  changed_files_summary: true
  path_filters:
    - "!**/*.lock"
    - "!**/*.generated.*"
    - "!**/dist/**"
    - "!**/node_modules/**"
    - "!**/coverage/**"
    - "!**/__snapshots__/**"

chat:
  auto_reply: true

knowledge_base:
  opt_out: false
  learnings:
    scope: auto

CodeRabbit reads this configuration on every PR and applies your settings immediately – no restart, reinstallation, or dashboard change is required. Each repository can have its own .coderabbit.yaml with different settings, which is especially useful for organizations that have repositories with different languages, frameworks, or review standards.

Review profiles explained

The profile setting controls how verbose and strict CodeRabbit’s reviews are:

• chill – Focuses only on significant issues like bugs, security vulnerabilities, and logic errors. Produces fewer comments and is the best starting point for teams new to AI code review. Most teams find this strikes the right balance between catching real issues and avoiding noise.
• assertive – Reviews more comprehensively, covering style, naming, documentation, performance, and best practices in addition to bugs and security. Produces more comments and is best for teams that want thorough feedback on every PR.
• followup – Similar to assertive but also checks whether previous review comments were addressed in subsequent commits. This is the strictest mode and is best for teams that want to ensure review feedback is acted on.

Start with chill and switch to assertive after your team is comfortable with the tool – typically after two to four weeks.

Path filters for excluding files

The path_filters section controls which files CodeRabbit analyzes. Use negation patterns (prefixed with !) to exclude files that do not benefit from AI review:

reviews:
  path_filters:
    - "!**/*.lock"           # Package lock files
    - "!**/*.min.js"         # Minified JavaScript
    - "!**/dist/**"          # Build output
    - "!**/vendor/**"        # Vendored dependencies
    - "!**/*.generated.*"    # Auto-generated code
    - "!**/migrations/**"    # Database migrations (optional)
    - "!**/*.snap"           # Test snapshots

Excluding these files reduces noise and speeds up reviews because CodeRabbit does not spend time analyzing files where its feedback would not be actionable.

Advanced configuration

Once you have the basic .coderabbit.yaml working, these advanced settings let you get more value from the CodeRabbit GitHub integration.

Review instructions for specific paths

The path_instructions feature is one of CodeRabbit’s most powerful capabilities. It lets you provide natural language instructions that CodeRabbit follows when reviewing files matching specific patterns:

reviews:
  path_instructions:
    - path: "src/api/**"
      instructions: |
        Review all API endpoints for:
        - Input validation on all request parameters
        - Proper error handling with appropriate HTTP status codes
        - Authentication and authorization checks
        - Rate limiting considerations
    - path: "src/db/**"
      instructions: |
        Review database code for:
        - SQL injection prevention using parameterized queries
        - Proper connection handling and cleanup
        - Transaction usage for multi-step writes
    - path: "**/*.test.*"
      instructions: |
        For test files, focus on:
        - Edge case coverage
        - Proper assertion usage
        - Mock cleanup and test isolation

This means your API code gets reviewed for authentication gaps, your database code gets checked for SQL injection, and your tests get evaluated for missing edge cases – all automatically based on the file path.

Global review instructions

Add a top-level instructions field to guide CodeRabbit’s behavior across all files in the repository:

reviews:
  profile: assertive
  instructions: |
    This is a TypeScript backend service using Express and PostgreSQL.
    Our conventions:
    - All async functions must use try/catch with proper error logging
    - Database queries must use parameterized statements
    - API responses follow the format: { data, error, meta }
    - Do not comment on import ordering (handled by ESLint)
    - Do not comment on line length (handled by Prettier)
    - Focus on bugs, security issues, and logic errors

Global instructions are especially useful for preventing false positives. If your linter already handles import ordering and your formatter handles line length, telling CodeRabbit to skip those categories eliminates a common source of noise.

Auto-review settings

Control whether CodeRabbit reviews PRs automatically or only when requested:

reviews:
  auto_review:
    enabled: true
    drafts: false
    base_branches:
      - main
      - develop

Setting drafts: false means CodeRabbit skips draft PRs, which is the default behavior. The base_branches setting limits auto-review to PRs targeting specific branches – useful if you only want CodeRabbit to review PRs going into your main branches and not PRs between feature branches.

Path filters for monorepos

If your repository is a monorepo with multiple services, combine path instructions with path filters to apply different review criteria to each service:

reviews:
  path_instructions:
    - path: "services/auth/**"
      instructions: |
        Authentication service - focus on token validation,
        password hashing (bcrypt only), and OWASP compliance
    - path: "services/payments/**"
      instructions: |
        Payment service - focus on PCI DSS patterns,
        idempotency of operations, and decimal precision
    - path: "packages/shared/**"
      instructions: |
        Shared library - focus on backward compatibility,
        type safety, and performance implications

Each service gets reviewed with context-specific criteria, which dramatically improves review quality compared to generic instructions applied uniformly across a monorepo.

Integrating with issue trackers

CodeRabbit can read linked Jira or Linear issues to understand the context behind a PR. When it knows the ticket description and acceptance criteria, it can validate whether the code changes actually address the requirements:

integrations:
  jira:
    enabled: true
    project_keys:
      - "BACKEND"
      - "FRONTEND"
  linear:
    enabled: true

Troubleshooting the CodeRabbit GitHub integration

If the integration is not working as expected, work through these issues in order.

CodeRabbit is not reviewing PRs

1. Check the GitHub App installation. Go to your GitHub organization settings, then Applications, then Installed GitHub Apps. Verify that CodeRabbit is listed and has access to the repository. If you chose “Only select repositories” during installation, the repository might not be included.
2. Check repository activation. Log in to app.coderabbit.ai and verify that the repository is toggled on in the Repositories tab.
3. Check if the PR is a draft. CodeRabbit skips draft PRs by default. Mark the PR as ready for review, or add drafts: true under auto_review in your .coderabbit.yaml.
4. Check path filters. If your .coderabbit.yaml excludes all the paths that changed in the PR, CodeRabbit will have nothing to review.
5. Check rate limits. On the free tier, you are limited to 4 PR reviews per hour and 200 files per hour. If you have hit these limits, reviews are queued until capacity is available.
6. Check webhook delivery. In your GitHub repository settings, navigate to Webhooks and inspect recent deliveries for the CodeRabbit webhook. Failed deliveries indicate a connectivity issue.

YAML configuration is not taking effect

1. Verify the file name is exactly .coderabbit.yaml – not .coderabbit.yml and not coderabbit.yaml
2. Verify the file is in the repository root, not in a subdirectory
3. Verify the file is on the default branch (usually main), not on a feature branch
4. Validate the YAML syntax using an online YAML validator – indentation errors are the most common cause
5. Comment @coderabbitai review on a PR to force CodeRabbit to re-read the configuration

Permission errors

1. For personal accounts: Only the account owner can install GitHub Apps. Verify you own the account.
2. For organizations: App installations require organization owner approval. If you see “Request” instead of “Install,” the request is pending approval from your org owner.
3. For repositories with access restrictions: Some organizations use GitHub App policies to restrict which apps can access specific repositories. Check your organization settings under Third-party access.

Too many or irrelevant comments

1. Switch the review profile from assertive to chill
2. Add path filters to exclude generated files, lock files, and build artifacts
3. Add global instructions telling CodeRabbit what not to comment on
4. Reply to irrelevant comments with explanations – CodeRabbit learns from your feedback
5. Enable the knowledge base so CodeRabbit builds repository-specific context over time

Alternative tools for GitHub code review

If CodeRabbit does not fit your needs, several alternatives provide AI-powered code review for GitHub repositories.

CodeAnt AI ($24-40/user/month) is a Y Combinator-backed platform that combines AI PR reviews with SAST scanning, secret detection, IaC security, and DORA metrics in a single tool. The Basic plan at $24/user/month covers AI PR reviews with line-by-line feedback across 30+ languages. The Premium plan at $40/user/month adds security scanning, compliance reporting (SOC 2, HIPAA), and engineering dashboards. CodeAnt AI is a strong alternative for teams that want to consolidate multiple security and review tools into one platform rather than running CodeRabbit alongside separate SAST tools.

GitHub Copilot code review is built directly into the GitHub PR interface and requires no additional app installation for teams already on Copilot Business or Enterprise plans. It provides AI-powered review comments but offers less configuration depth than CodeRabbit – there is no equivalent to .coderabbit.yaml path instructions or review profiles.

PR-Agent by Qodo is an open-source option that runs as a GitHub Action. It gives you full control over the LLM provider and can be self-hosted for teams with strict data residency requirements. You pay for your own LLM API keys rather than a per-user subscription.

For a comprehensive comparison of all available options, see our guide on CodeRabbit alternatives. If you are specifically interested in using GitHub Actions for code review automation, our AI code review in GitHub Actions guide covers multiple tools with production-ready workflow configurations.

Conclusion

The CodeRabbit GitHub integration gives your team instant AI-powered code review on every pull request with a setup process that takes under 10 minutes. Install the GitHub App, select your repositories, open a PR, and CodeRabbit starts posting review comments – no CI/CD configuration, no workflow files, no API keys.

The real value comes from customization. A .coderabbit.yaml file with review instructions tailored to your codebase structure, coding conventions, and security requirements turns CodeRabbit from a generic code reviewer into a context-aware team member that knows your API routes need authentication checks and your database code needs parameterized queries.

Start with the chill review profile on one or two repositories. Let your team interact with CodeRabbit’s comments for two weeks. Then tune the configuration based on real feedback – add path instructions for your most critical directories, exclude files that generate noise, and expand to additional repositories as confidence grows.

For next steps, explore our CodeRabbit setup guide for additional onboarding tips, our CodeRabbit pricing guide to understand when it makes sense to upgrade from the free tier, and our AI code review in GitHub Actions guide to see how CodeRabbit fits alongside other tools in your review pipeline.

Further Reading

• CodeRabbit Azure DevOps: Setting Up AI Code Review
• CodeRabbit Configuration: Complete .coderabbit.yaml Reference (2026)
• CodeRabbit GitLab Integration: Step-by-Step Configuration (2026)
• How to Use CodeRabbit for Automated Pull Request Reviews
• Best AI Code Review Tools in 2026 – Expert Picks

Frequently Asked Questions

How do I integrate CodeRabbit with GitHub?

Go to coderabbit.ai and click Get Started Free. Sign in with your GitHub account and authorize the CodeRabbit GitHub App. Select the repositories you want CodeRabbit to review – either all repositories or specific ones. Once installed, CodeRabbit automatically reviews every new pull request within minutes. No GitHub Actions workflow or CI/CD pipeline is required.

Is the CodeRabbit GitHub App free?

Yes. CodeRabbit offers a free tier that covers unlimited public and private repositories with AI-powered PR summaries and review comments. The free tier includes rate limits of 200 files per hour and 4 PR reviews per hour. The Pro plan at $24/user/month removes these limits and adds features like auto-fix suggestions and 40+ built-in linters. Pro is free forever for open-source repositories.

What GitHub permissions does CodeRabbit need?

CodeRabbit requires read access to repository contents, metadata, and pull requests so it can analyze code changes. It also needs write access to pull request comments and checks so it can post review feedback. Finally, it requires webhook events for pull request creation and updates so it knows when to start a review. These are standard permissions for any code review GitHub App.

Does CodeRabbit work with GitHub Enterprise?

Yes. CodeRabbit supports GitHub Enterprise Cloud out of the box. For GitHub Enterprise Server (self-hosted), CodeRabbit offers an Enterprise plan with on-premises deployment options. Contact the CodeRabbit sales team to set up integration with self-hosted GitHub instances that are not publicly accessible.

How do I configure CodeRabbit for specific GitHub repositories?

You can select specific repositories during the GitHub App installation by choosing Only select repositories instead of All repositories. After installation, toggle individual repositories on or off from the CodeRabbit dashboard at app.coderabbit.ai. You can also create a .coderabbit.yaml file in each repository root to apply repository-specific review settings.

What is the .coderabbit.yaml file and where does it go?

The .coderabbit.yaml file is CodeRabbit’s repository-level configuration file. Place it in the root of your repository on the default branch (usually main). It controls review behavior including the review profile (chill, assertive, or followup), path filters for excluding files, path-specific review instructions, and feature toggles like sequence diagrams and PR summaries. CodeRabbit reads this file on every PR automatically.

Why is CodeRabbit not reviewing my GitHub pull requests?

Check these causes in order: the CodeRabbit GitHub App may not be installed on the repository, the repository may be toggled off in your CodeRabbit dashboard, the PR may be marked as a draft (CodeRabbit skips drafts by default), or your path_filters in .coderabbit.yaml may exclude the changed files. Also verify that the app has the necessary permissions in your GitHub Settings under Applications. On the free tier, exceeding 4 reviews per hour queues additional reviews.

Can I use CodeRabbit alongside GitHub Copilot code review?

Yes. CodeRabbit and GitHub Copilot code review serve complementary roles and can run simultaneously on the same repository without conflicts. CodeRabbit provides deeper contextual analysis with customizable review instructions and learning capabilities, while Copilot code review is tightly integrated into the GitHub UI. Many teams run both to get broader coverage.

How do I exclude files from CodeRabbit reviews on GitHub?

Add a path_filters section under reviews in your .coderabbit.yaml file. Use negation patterns with an exclamation mark prefix to exclude paths. For example, adding ‘!/*.lock’ excludes lock files, ‘!/dist/‘ excludes build output, and ‘!/node_modules/**’ excludes dependencies. Changes to path filters take effect on the next PR without any restart.

Does CodeRabbit support GitHub Actions workflows?

CodeRabbit does not run as a GitHub Actions step. Instead, it runs as a standalone GitHub App on its own infrastructure, which means it does not consume your GitHub Actions minutes or require YAML workflow files. It works alongside any existing GitHub Actions workflows you have. If you prefer a GitHub Actions-based approach, consider alternatives like PR-Agent which runs as an Action.

How long does CodeRabbit take to review a GitHub pull request?

CodeRabbit typically completes its review within 1 to 5 minutes after a pull request is opened or updated. Small PRs with fewer than 100 changed lines usually finish in under 2 minutes. Larger PRs with thousands of lines may take up to 5 minutes. On the free tier, if you exceed 4 reviews per hour, additional reviews are queued until capacity is available.

What alternatives to CodeRabbit exist for GitHub code review?

Top alternatives include CodeAnt AI ($24-40/user/month) which combines AI PR reviews with SAST and DORA metrics, GitHub Copilot code review which is built into GitHub natively, PR-Agent (by Qodo) which is open source and runs as a GitHub Action, and Sourcery which specializes in Python code review. Each tool has different strengths depending on your team size, language, and compliance needs.

Originally published at aicodereview.cc

I got tired of noisy charts and vague signals.

So I built Orra, a tarot app powered by Pyth that turns real-time market data into verifiable readings.

Most tools give raw numbers.

Most tarot apps give vibes without proof.

I wanted both intuition and transparency in one flow.

What Orra Does

• Lets users pick an asset and go through an adaptive question flow
• Anchors each reading to live Pyth Price Feed context
• Triggers card draws with Pyth Entropy
• Shows an on-chain receipt linking snapshot and draw result
• Generates a share-ready reading image for socials or friends

Why I Built It

I wanted a product that feels human, not just another chart dashboard, while still being technically credible. Orra is my attempt to combine ritual-style UX with verifiable on-chain mechanics.

Product Flow

1) Dashboard

Users land on the dashboard, search assets, and review live market context before starting a reading.

2) Wallet Connect

Users connect wallet to enter the reading flow.

3) Asset Selection

Users choose the asset, and Orra confirms the live Pyth feed.

4) Adaptive Question Flow

Users answer guided questions about market situation, timing, and focus. The path adapts by asset category.

5) Oracle Commit

Users confirm the fee and trigger the draw. Orra commits a Pyth snapshot on-chain and requests Entropy randomness.

6) Card Draw

A card is selected from Entropy output. Orientation (upright/reversed) is also determined deterministically from the same random value.

7) Reading Result + Proof

The final view shows the card, interpretation, and on-chain receipt proving the snapshot and draw linkage.

8) Shareable Reading Card

Users can download a clean reading card image to share externally.

Live Demo

https://orra-pyth.vercel.app/

Feedback I Want

I would love feedback on:

• UX clarity through the full flow
• Trust and proof presentation
• Interpretation quality and tone
• Features that would make this more useful daily

Orra. Read the market. Read your path.